Wentao Li

FedGMMAT enables privacy-preserving genetic association testing across institutions using federated learning and homomorphic encryption.

This paper develops a privacy-aware kinship inference method for admixed populations using reference panels and homomorphic encryption to protect genetic data.

The paper introduces a secure imputation server that provides encrypted genotype imputation with provable privacy, demonstrating its utility through benchmarking and simulated GWAS.